| import numpy as np |
| import DeepDeformationMapRegistration.utils.constants as C |
|
|
| class SummaryDictionary: |
| def __init__(self, model, batch_size, accumulative_gradients_step=None): |
| self.train_names = model.metrics_names |
| self.val_names = ['val_'+n for n in self.train_names] |
| self.batch_size = batch_size |
| self.acc_grad_step = accumulative_gradients_step |
| self._reset() |
|
|
| def _reset(self): |
| self.summary_dict = {'size': self.batch_size} |
| if self.acc_grad_step is not None: |
| self.summary_dict = {'accumulative_grad_step': self.acc_grad_step} |
| for k in self.train_names + self.val_names: |
| self.summary_dict[k] = list() |
|
|
| def on_train_batch_end(self, values): |
| for k, v in zip(self.train_names, values): |
| self.summary_dict[k].append(v) |
|
|
| def on_validation_batch_end(self, values): |
| for k, v in zip(self.val_names, values): |
| self.summary_dict[k].append(v) |
|
|
| def on_epoch_end(self): |
| for k, v in self.summary_dict.items(): |
| self.summary_dict[k] = np.asarray(v).mean() |
|
|
| ret_val = self.summary_dict.copy() |
| self._reset() |
| return ret_val |
|
|
|
|
| def named_logs(model, logs, validation=False): |
| result = {'size': C.BATCH_SIZE} |
| for l in zip(model.metrics_names, logs): |
| k = ('val_' if validation else '') + l[0] |
| result[k] = l[1] |
| return result |
|
|
